Merge branch 'master' into devel

GPy/__init__.py

@@ -28,11 +28,11 @@ try:
     #Get rid of nose dependency by only ignoring if you have nose installed
     from nose.tools import nottest
     @nottest
-    def tests():
-        Tester(testing).test(verbose=10)
+    def tests(verbose=10):
+        Tester(testing).test(verbose=verbose)
 except:
-    def tests():
-        Tester(testing).test(verbose=10)
+    def tests(verbose=10):
+        Tester(testing).test(verbose=verbose)
 
 def load(file_path):
     """

GPy/__version__.py

@@ -1 +1 @@
-__version__ = "0.8.3"
+__version__ = "0.8.4"

GPy/examples/coreg_example.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 try:
-    import pylab as pb
+    from matplotlib import pyplot as pb
 except:
     pass
 import GPy

GPy/examples/non_gaussian.py

@@ -77,7 +77,7 @@ def student_t_approx(optimize=True, plot=True):
     debug=True
     if debug:
         m4.optimize(messages=1)
-        import pylab as pb
+        from matplotlib import pyplot as pb
         pb.plot(m4.X, m4.inference_method.f_hat)
         pb.plot(m4.X, m4.Y, 'rx')
         m4.plot()

GPy/examples/regression.py

@@ -5,7 +5,7 @@
 Gaussian Processes regression examples
 """
 try:
-    import pylab as pb
+    from matplotlib import pyplot as pb
 except:
     pass
 import numpy as np

GPy/kern/_src/independent_outputs.py

@@ -105,7 +105,7 @@ class IndependentOutputs(CombinationKernel):
         if X2 is None:
             # TODO: make use of index_to_slices
             # FIXME: Broken as X is already sliced out
-            print("Warning, gradients_X may not be working, I believe X has already been sliced out by the slicer!")
+            # print("Warning, gradients_X may not be working, I believe X has already been sliced out by the slicer!")
             values = np.unique(X[:,self.index_dim])
             slices = [X[:,self.index_dim]==i for i in values]
             [target.__setitem__(s, kern.gradients_X(dL_dK[s,s],X[s],None))

GPy/models/gradient_checker.py

@@ -251,7 +251,7 @@ class HessianChecker(GradientChecker):
             print(grad_string)
 
             if plot:
-                import pylab as pb
+                from matplotlib import pyplot as pb
                 fig, axes = pb.subplots(2, 2)
                 max_lim = numpy.max(numpy.vstack((analytic_hess, numeric_hess)))
                 min_lim = numpy.min(numpy.vstack((analytic_hess, numeric_hess)))

GPy/plotting/__init__.py

@@ -4,4 +4,8 @@
 try:
     from . import matplot_dep
 except (ImportError, NameError):
-    print('Fail to load GPy.plotting.matplot_dep.')
+    # Matplotlib not available
+    import warnings
+    warnings.warn(ImportWarning("Matplotlib not available, install newest version of Matplotlib for plotting"))
+    #sys.modules['matplotlib'] =
+    #sys.modules[__name__+'.matplot_dep'] = ImportWarning("Matplotlib not available, install newest version of Matplotlib for plotting")

GPy/plotting/matplot_dep/Tango.py

@@ -3,7 +3,7 @@
 
 
 import matplotlib as mpl
-import pylab as pb
+from matplotlib import pyplot as pb
 import sys
 #sys.path.append('/home/james/mlprojects/sitran_cluster/')
 #from switch_pylab_backend import *
@@ -159,7 +159,7 @@ cdict_Alu = {'red' :((0./5,colorsRGB['Aluminium1'][0]/256.,colorsRGB['Aluminium1
 # cmap_BGR = mpl.colors.LinearSegmentedColormap('TangoRedBlue',cdict_BGR,256)
 # cmap_RB = mpl.colors.LinearSegmentedColormap('TangoRedBlue',cdict_RB,256)
 if __name__=='__main__':
-    import pylab as pb
+    from matplotlib import pyplot as pb
     pb.figure()
     pb.pcolor(pb.rand(10,10),cmap=cmap_RB)
     pb.colorbar()

GPy/plotting/matplot_dep/base_plots.py

@@ -4,7 +4,7 @@
 
 try:
     #import Tango
-    import pylab as pb
+    from matplotlib import pyplot as pb
 except:
     pass
 import numpy as np

GPy/plotting/matplot_dep/inference_plots.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 try:
-    import pylab as pb
+    from matplotlib import pyplot as pb
 except:
     pass
 #import numpy as np

GPy/plotting/matplot_dep/kernel_plots.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-import pylab as pb
+from matplotlib import pyplot as pb
 import Tango
 from matplotlib.textpath import TextPath
 from matplotlib.transforms import offset_copy

GPy/plotting/matplot_dep/mapping_plots.py

@@ -4,7 +4,7 @@
 import numpy as np
 try:
     import Tango
-    import pylab as pb
+    from matplotlib import pyplot as pb
 except:
     pass
 from base_plots import x_frame1D, x_frame2D

GPy/plotting/matplot_dep/maps.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 import numpy as np
 try:
-    import pylab as pb
+    from matplotlib import pyplot as pb
     from matplotlib.patches import Polygon
     from matplotlib.collections import PatchCollection
     #from matplotlib import cm

GPy/plotting/matplot_dep/priors_plots.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 try:
-    import pylab as pb
+    from matplotlib import pyplot as pb
 except:
     pass
 

GPy/plotting/matplot_dep/svig_plots.py

@@ -2,7 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 import numpy as np
-import pylab as pb
+from matplotlib import pyplot as pb
 
 
 def plot(model, ax=None, fignum=None, Z_height=None, **kwargs):

GPy/plotting/matplot_dep/variational_plots.py

@@ -1,4 +1,4 @@
-import pylab as pb, numpy as np
+from matplotlib import pyplot as pb, numpy as np
 
 def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
     """

GPy/testing/likelihood_tests.py

@@ -7,7 +7,6 @@ from GPy.models import GradientChecker
 import functools
 import inspect
 from GPy.likelihoods import link_functions
-from GPy.core.parameterization import Param
 from functools import partial
 fixed_seed = 7
 
@@ -799,7 +798,7 @@ class LaplaceTests(unittest.TestCase):
         post_mean_approx, post_var_approx, = m2.predict(X)
 
         if debug:
-            import pylab as pb
+            from matplotlib import pyplot as pb
             pb.figure(5)
             pb.title('posterior means')
             pb.scatter(X, post_mean, c='g')

GPy/util/datasets.py

@@ -374,7 +374,7 @@ def football_data(season='1314', data_set='football_data'):
     data_resources[data_set_season]['files'] = [files]
     if not data_available(data_set_season):
         download_data(data_set_season)
-    import pylab as pb
+    from matplotlib import pyplot as pb
     for file in reversed(files):
         filename = os.path.join(data_path, data_set_season, file)
         # rewrite files removing blank rows.